You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.In previous model training and tuning runs, many models showed similar performance.You need to select an early termination policy that meets the following requirements:-accounts for the performance of all previous runs when evaluating the current run avoids comparing the current run with only the best performing run to dateWhich two early termination policies should you use? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.
Answer(s): A,D
The Median Stopping policy computes running averages across all runs and cancels runs whose best performance is worse than the median of the running averages.If no policy is specified, the hyperparameter tuning service will let all training runs execute to completion.Incorrect Answers:B: BanditPolicy defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation.The Bandit policy takes the following configuration parameters: slack_factor: The amount of slack allowed with respect to the best performing training run. This factor specifies the slack as a ratio.D: The Truncation selection policy periodically cancels the given percentage of runs that rank the lowest for their performance on the primary metric. The policy strives for fairness in ranking the runs by accounting for improving model performance with training time. When ranking a relatively young run, the policy uses the corresponding (and earlier) performance of older runs for comparison. Therefore, runs aren't terminated for having a lower performance because they have run for less time than other runs.
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.medianstoppingpolicy https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.truncationselectionpolicy https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.banditpolicy
HOTSPOT (Drag and Drop is not supported)You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.You need to review the models and explain how each model makes decisions.Which explainer modules should you use? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Meta explainers automatically select a suitable direct explainer and generate the best explanation info based on the given model and data sets. The meta explainers leverage all the libraries (SHAP, LIME, Mimic, etc.) that we have integrated or developed. The following are the meta explainers available in the SDK:Tabular Explainer: Used with tabular datasets.Text Explainer: Used with text datasets.Image Explainer: Used with image datasets.Box 1: Tabular Box 2: Text Box 3: Image Incorrect Answers:Hierarchical Attention Network (HAN)HAN was proposed by Yang et al. in 2016. Key features of HAN that differentiates itself from existing approaches to document classification are (1) it exploits the hierarchical nature of text data and (2) attention mechanism is adapted for document classification.
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298
HOTSPOT (Drag and Drop is not supported)You have a dataset that includes home sales data for a city. The dataset includes the following columns.Each row in the dataset corresponds to an individual home sales transaction.You need to use automated machine learning to generate the best model for predicting the sales price based on the features of the house.Which values should you use? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
Box 1: Regression Regression is a supervised machine learning technique used to predict numeric values.Box 2: Price
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer
You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder.The experiment fails.You need to troubleshoot the failed experiment.What are two possible ways to achieve this goal? Each correct answer presents a complete solution.
Answer(s): B,D
Use get_details_with_logs() to fetch the run details and logs created by the run.You can monitor Azure Machine Learning runs and view their logs with the Azure Machine Learning studio.Incorrect Answers:A: You can view the metrics of a trained model using run.get_metrics().E: get_output() gets the output of the step as PipelineData.
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-view-training-logs
DRAG DROP (Drag and Drop is not supported)You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Service (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.Select and Place:
Step 1: Create and start a Compute InstanceTo train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.There are four kinds of compute resource you can create:Compute Instances: Development workstations that data scientists can use to work with data and models.Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.Inference Clusters: Deployment targets for predictive services that use your trained models.Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.Step 2: Create and run a training pipeline..After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipelineStep 3: Create and run a real-time inference pipelineAfter creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/
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